Several rules of thumb for the minimum sample size of structural equation models have been proposed. A widely-accepted ratio of sample size to estimated parameters is N:p = 5:1 (Bentler & Chou, 1987). To examine under what conditions this rule-of-thumb holds, we ran Monte-Carlo simulations for a structural-equation model having three exogenous latent variables that predicted a dependent variable via an endogenous regressor. We varied (a) the number of observations (from 40 to 200); (b) the number of latent variables indicators (from 2 to 6); (c) the correlations between factors (from 0.2 to 0.8) and the degree of endogeneity affecting the endogenous regressor (high or low). Results show that ML estimates are still consistent across-the-board. However, in small sample size conditions model convergence rates were low and the chi-square test of model fit tended to over-reject correctly specified models. We found that a correction to the chi-square proposed by Swain (1976) better approximates the chi-square distribution at small sample sizes and reduced rejection rates close to the Type I error rate (5%). Finally, we found that the Hausman (1978) endogeneity test had very low power at small sample sizes. Based on these results we make several recommendations for applied researchers.